Automated extraction of clinical measures from videos of oculofacial disorders using machine learning: feasibility, validity and reliability

Automated extraction of clinical measures from videos of oculofacial disorders using machine learning: feasibility, validity and reliability

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To determine the feasibility, validity and reliability of automatically extracting clinically meaningful eyelid measurements from consumer-grade videos of individuals with oculofacial


disorders.


A custom computer program was designed to automatically extract clinical measures from consumer-grade videos. This program was applied to publicly available videos of individuals with


oculofacial disorders, and age-matched controls. The primary outcomes were margin reflex distance 1 (MRD1) and 2 (MRD2), blink lagophthalmos, and ocular surface area exposure. Test-retest


reliability was evaluated using Bland–Altman analysis to compare the agreement in obtained measures between separate videos of the same individual taken within 48 h of each other.


MRD1 was reduced in individuals with ptosis versus controls (2.2 mm versus 3.4 mm, p